L1/L2 loss are commonly used to train the network, while they only constrain the upper bound of the solution space and the lower bound is neglected, thus they are difficult to reconstruct a high-quality MR image. To constrain the upper bound and lower bound of the solution space, we introduce contrastive learning, which can pull anchors close to positive samples while push away negative samples. For contrastive learning, how to select positive and negative samples and how to find the potential feature space are very important. In our work, we denote the output of CLGNet and the clear image as the anchor and the positive sample, respectively. For negative samples, following [1]}, we sample \(K\)  undersampled MR images from the same mini-batch as negative samples. For the latent feature space, VGG19 [2]} is adopted to extract the feature of anchor (\(O\) ), positive (\(GT\) ), and negative (\(O_{Neg}\) ) samples. Hence, the contrastive loss can be expressed as:
\(\small L_{CL}=\sum _{i}^{N} \sum _{j}^{M} \lambda _{j} \frac{||(\phi _{j}(O^{(i)}), \phi _{j}(GT^{(i)})||_1}{\sum _{k}^{K} ||(\phi _{j}(O^{(i)}), \phi _{j}(O_{N e g}^{(k)})||_1},\) 
